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Unit outline_

FINC6009: Portfolio Theory and its Applications

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

This unit covers several aspects of modern/post-modern portfolio theory. An introduction to mathematical optimisation techniques in the presence of uncertainty is covered and results from modern portfolio theory to the Capital Asset Pricing Model derived. The unit also examines other popular models such as the Arbitrage Pricing Theory and Black-Litterman Model and concludes with some topical examples from industry. There is a degree of mathematical sophistication associated with this unit and consequently, students should be comfortable with a mathematical approach. However, the required mathematical tools are covered in the unit.

Unit details and rules

Academic unit Finance
Credit points 6
Prerequisites
? 
FINC5001 or FINC6000
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Hamish Malloch, hamish.malloch@sydney.edu.au
Lecturer(s) Hamish Malloch, hamish.malloch@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final exam
Closed book exam
40% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO4
Assignment Assignment 1
Computational assignment
30% Week 06
Due date: 06 Sep 2023 at 23:59

Closing date: 16 Sep 2023
Completed template
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 2
Written report
30% Week 11
Due date: 18 Oct 2023 at 23:59

Closing date: 28 Oct 2023
3 pages max
Outcomes assessed: LO1 LO2 LO3 LO4

Assessment summary

  • Assignment 1: Students will work individually to perform a variety of calculations involving real world data. These results will be submitted in a standardised template. Topics up to and incluing week 5 may be covered.
  • Assignment 2: Students will work individually to address a real world investment issue. Students will be required to perform an analysis and present their results via a written report. Topics up to and including week 10 may be covered.
  • Final exam: This exam will cover all material in the unit. It will consist of calculation/derivation type questions as well as short answer and discussion questions.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Students will be penalised 5% of the maximum mark per day late. Assessments will not be accepted or marked after the closing date which is set 10 calendar days after the due date.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction and maths/stats review Lecture (2 hr) LO1
Week 02 Risk, return and utility Lecture (2 hr) LO1 LO2
Introduction and maths/stats review Tutorial (1 hr) LO1
Week 03 Mean-Variance portfolio theory Lecture (2 hr) LO1 LO2 LO3 LO4
Risk, return and utility Tutorial (1 hr) LO1 LO2
Week 04 Capital asset pricing model (CAPM) Lecture (2 hr) LO1 LO2 LO3 LO4
Mean-Variance portfolio theory Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 05 Index models Lecture (2 hr) LO1 LO2 LO3 LO4
Capital asset pricing model (CAPM) Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 06 Arbitrage pricing theory Lecture (2 hr) LO1 LO2 LO3 LO4
Index models Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 07 Traditional quantitative equity portfolio management (QEPM) Lecture (2 hr) LO1 LO2 LO3 LO4
Arbitrage pricing theory Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 08 The Black-Litterman framework Lecture (2 hr) LO1 LO2 LO3 LO4
Traditional quantitative equity portfolio management (QEPM) Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 09 Anomalies and smart beta Lecture (2 hr) LO1 LO2 LO3 LO4
The Black-Litterman framework Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 10 Efficient markets & behavioural finance Lecture (2 hr) LO2 LO4
Anomalies and smart beta Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 11 Advanced topics in portfolio theory I Lecture (2 hr) LO1 LO2 LO3 LO4
Efficient markets & behavioural finance Tutorial (1 hr) LO2 LO4
Week 12 Advanced topics in portfolio theory II Lecture (2 hr) LO1 LO2 LO3 LO4
Advanced topics in portfolio theory I Tutorial (1 hr) LO1 LO2 LO3 LO4
Week 13 Review & exam preparation Lecture (2 hr) LO1 LO2 LO3 LO4
Advanced topics in portfolio theory II Tutorial (1 hr) LO1 LO2 LO3 LO4

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

The main reading material for this course are the online notes provided in Canvas. Some texts that students may find useful are:

  • Bodie, Kane and Marcus, “Investments”, McGraw-Hill Irwin, 11th edition, 2017.
  • Elton, Gruber, Brown and Goetzman, “Modern Portfolio Theory and Investment Analysis”, Wiley, 9th edition, 2014.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. describe portfolio and investment problems in a mathematical context to enable an advanced approach to portfolio construction.
  • LO2. provide a detailed explanation of the mathematical and economic principles of risk and return and demonstrate how that relates to portfolio theory.
  • LO3. apply the principles of portfolio theory to solve practical investment problems utilising EXCEL and/or other software packages
  • LO4. contrast different approaches to portfolio theory and explain the strengths and weaknesses of the various models covered.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

In response to student feedback requesting additional learning material, a comprehensive set of student notes will be made available online. These notes include Python code that implements practical examples and other interactive elements.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.